3D Point Cloud Primitive Fitting via Distance Fields
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Solution Overview
Problem
Existing methods for fitting primitive shapes to 3D point clouds are time-consuming due to the need to traverse or search all points in the cloud for score evaluation, making them inefficient for large-scale scenes.
Innovation Solution
Representing 3D point clouds as a distance field allows for efficient score computation and refinement using a RANSAC framework, where the distance field provides distance and gradient information for each point, enabling fast hypothesis evaluation and parameter refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw 3D point clouds are used for score evaluation in RANSAC framework, then measurement precision is maintained, but processing time increases significantly
Solution Approach 1:
The patent pre-computes the distance field from the 3D point cloud before applying the RANSAC framework. This preliminary action transforms the raw point cloud into a distance field representation that enables fast score evaluation during hypothesis testing, without requiring traversal or search operations on the original point cloud during the iterative RANSAC process.
Solution Approach 2:
The patent introduces a distance field as an intermediary data structure between the raw 3D point cloud and the RANSAC score evaluation process. The distance field acts as a mediator that provides efficient random access to distance information, allowing the RANSAC framework to evaluate hypothesis scores in constant time without directly accessing the original point cloud.
2Measurement precision
If all points in 3D point cloud are traversed for each hypothesized primitive shape, then measurement precision is ensured, but productivity decreases
Solution Approach 1:
The distance field is pre-computed from the 3D point cloud before the RANSAC framework is applied. This preliminary transformation enables O(1) score evaluation for each hypothesized primitive shape, eliminating the need to traverse all points during the iterative hypothesis testing process and significantly improving processing speed.
Solution Approach 2:
The patent creates a distance field copy or transformation of the original 3D point cloud data. This copied representation stores pre-computed distance information that can be queried efficiently during RANSAC score evaluation, avoiding repeated traversal of the original point cloud while preserving the necessary geometric information.
3Measurement precision
If 3D point clouds are stored and processed separately for each point, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The patent merges the individual point information into a unified distance field representation. Instead of storing and processing each 3D point separately, the distance field combines all point information into a single data structure that provides efficient query capabilities, reducing memory overhead and simplifying the processing architecture.
Solution Approach 2:
The patent transforms the 3D point cloud data into a distance field representation that adds a distance dimension to the spatial coordinates. This dimensional transformation reorganizes the data structure to enable efficient random access and querying operations, reducing the complexity of point-wise processing while maintaining measurement precision.
Data Source
AI summary
A method fits primitive shapes to a set of three-dimensional (3D) points by first converting the set of 3D points to a distance field. Each element in the distance field is associated with a distance to a nearest point in the set of 3D points. A set of two or more candidates are hypothesizing from the primitive shapes, and a score is determined for each candidate using the distance field. Then, the primitive shape to fit to the 3D points is selected from the candidates according to their scores.